Recent Advances in Artificial Neural Networks. Design and Applications
โ Scribed by Jain L., Fanelli A.M.
- Tongue
- English
- Leaves
- 358
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
ะะทะดะฐัะตะปัััะฒะพ CRC Press, 2000, -358 pp.
Neural networks are a new generation of information processing paradigms designed to mimic some of the behaviors of the human brain. These networks have gained tremendous popularity due to their ability to learn, recall and generalize from training data. A number of neural network paradigms have been reported in the last four decades, and in the last decade the neural networks have been refined and widely used by researchers and application engineers.The main purpose of this book is to report recent advances in neural network paradigms and their applications. It is impossible to include all recent advances in this book; hence, only a sample has been included.A neuro-symbolic hybrid intelligent architecture with applications
New radial basis neural networks and their application in a large-scale handwritten digit recognition problem
Efficient neural network-based methodology for the design of multiple classifiers
Learning fine motion in robotics: design and experiments
A new neural network for adaptive pattern recognition of multichannel input signals
Lateral priming adaptive resonance theory (LAPART)-2: innovation in ART
Neural network learning in a travel reservation domain
Recent advances in neural network applications in process control
Monitoring internal combustion engines by neural network based virtual sensing
Neural architectures of fuzzy Petri nets
โฆ Subjects
ะะฝัะพัะผะฐัะธะบะฐ ะธ ะฒััะธัะปะธัะตะปัะฝะฐั ัะตั ะฝะธะบะฐ;ะัะบััััะฒะตะฝะฝัะน ะธะฝัะตะปะปะตะบั;ะะตะนัะพะฝะฝัะต ัะตัะธ
๐ SIMILAR VOLUMES
Neural networks represent a new generation of information processing paradigms designed to mimic-in a very limited sense-the human brain. They can learn, recall, and generalize from training data, and with their potential applications limited only by the imaginations of scientists and engineers, the
<p>The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovat